{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>薪资</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>少儿编程讲师-Java/python</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>在校生/应届生</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python开发工程师</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python开发工程师</td>\n",
       "      <td>1.2-1.6万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python教研/教学主任</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>德企招Python 全栈工程师</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>少儿编程讲师-java/web/python</td>\n",
       "      <td>0.8-1万/月</td>\n",
       "      <td>在校生/应届生</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>计算机视觉算法工程师（python）(002083)</td>\n",
       "      <td>1.5-2万/月</td>\n",
       "      <td>无需经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>运维工程师（驻场银行）</td>\n",
       "      <td>0.9-1.3万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>测试工程师</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>量化投资研究员</td>\n",
       "      <td>1-2万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            职位          薪资     工作经验 学历要求\n",
       "0           少儿编程讲师-Java/python  0.8-1.2万/月  在校生/应届生   本科\n",
       "1                  Python开发工程师  0.8-1.2万/月     1年经验   本科\n",
       "2                  python开发工程师  1.2-1.6万/月     1年经验   本科\n",
       "3                Python教研/教学主任    1-1.5万/月   3-4年经验   本科\n",
       "4              德企招Python 全栈工程师    1-1.5万/月     2年经验   本科\n",
       "..                         ...         ...      ...  ...\n",
       "95      少儿编程讲师-java/web/python    0.8-1万/月  在校生/应届生   本科\n",
       "96  计算机视觉算法工程师（python）(002083)    1.5-2万/月     无需经验   本科\n",
       "97                 运维工程师（驻场银行）  0.9-1.3万/月     2年经验   本科\n",
       "98                       测试工程师    1-1.5万/月   3-4年经验   本科\n",
       "99                     量化投资研究员      1-2万/月     2年经验   本科\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"./python_job.txt\", sep=\"\\t\")\n",
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_xinzi(x):\n",
    "    if \"万/月\" in x: \n",
    "        low,high = x.replace(\"万/月\", \"\").split(\"-\")\n",
    "        return 10000*(float(high)+float(low))/2\n",
    "    if \"千/月\" in x: \n",
    "        low,high = x.replace(\"千/月\", \"\").split(\"-\")\n",
    "        return 1000*(float(high)+float(low))/2\n",
    "    if \"万/年\" in x: \n",
    "        low,high = x.replace(\"万/年\", \"\").split(\"-\")\n",
    "        return 10000*(float(high)+float(low))/2/12\n",
    "df[\"xinzi\"] = df[\"薪资\"].map(compute_xinzi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>薪资</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>xinzi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>少儿编程讲师-Java/python</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>在校生/应届生</td>\n",
       "      <td>本科</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python开发工程师</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python开发工程师</td>\n",
       "      <td>1.2-1.6万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python教研/教学主任</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>12500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>德企招Python 全栈工程师</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>12500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   职位          薪资     工作经验 学历要求    xinzi\n",
       "0  少儿编程讲师-Java/python  0.8-1.2万/月  在校生/应届生   本科  10000.0\n",
       "1         Python开发工程师  0.8-1.2万/月     1年经验   本科  10000.0\n",
       "2         python开发工程师  1.2-1.6万/月     1年经验   本科  14000.0\n",
       "3       Python教研/教学主任    1-1.5万/月   3-4年经验   本科  12500.0\n",
       "4     德企招Python 全栈工程师    1-1.5万/月     2年经验   本科  12500.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"xinzi\"]<100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85d1878290>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use({'figure.figsize':(12, 8)})\n",
    "\n",
    "sns.set_theme();\n",
    "sns.distplot(df[\"xinzi\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "sns.set(font='SimHei')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfont = matplotlib.font_manager.FontProperties(fname=\"/Library/Fonts/Arial Unicode.ttf\")#\"/Library/Fonts/Songti.ttc\")\n",
    "\n",
    "sns.set(font=myfont.get_name())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85d304ebd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = df.groupby(\"工作经验\")[\"xinzi\"].mean().reset_index()\n",
    "result.sort_values(by=\"xinzi\", inplace=True)\n",
    "sns.barplot(x=\"工作经验\", y=\"xinzi\", data=result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85d32499d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = df.groupby(\"学历要求\")[\"xinzi\"].mean().reset_index()\n",
    "result.sort_values(by=\"xinzi\", inplace=True)\n",
    "sns.barplot(x=\"学历要求\", y=\"xinzi\", data=result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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